Visualizing multiple evolution metrics

“…A number of approaches give summarized information on package relationships and their evolution: the Butterfly by Ducasse et al gives a high-level client/provider trend of package dependencies [DLP05]; Pzinger et al show the evolution of package metrics using Kiviat diagrams [PGFL05]; Chuah and Eick use rich glyphs to characterize software artifacts and their evolution (number of bugs, number of deleted lines, kind of language...) [CE98]. In particular, the timewheel exploits preattentive processing, and the infobug presents many different data sources in a compact way; finally, D'Ambros et al reveal package coupling by showing evolutions that are correlated in time [DL06].…”

Section: Related Workmentioning

confidence: 99%

Package Fingerprints: A visual summary of package interface usage

Abdeen

Ducasse

Pollet

et al. 2010

Information and Software Technology

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Context: Object-oriented languages such as Java, Smalltalk, and C++ structure their programs using packages. Maintainers of large systems need to understand how packages relate to each other, but this task is complex because packages often have multiple clients and play different roles (class container, code ownership. . . ). Several approaches have been proposed, among which the use of cohesion and coupling metrics. Such metrics help identify candidate packages for restructuring; however, they do not help maintainers actually understand the structure and interrelationships between packages.Objectives: In this paper, we use pre-attentive processing as the basis for package visualization and see to what extent it could be used in package understanding.Method: We present the package fingerprint, a 2D visualization of the references made to and from a package. The proposed visualization offers a semantically rich, but compact and zoomable views centered on packages. We focus on two views (incoming and outgoing references) that help users understand how the package under analysis is used by the system and how it uses the system.Results: We applied these views on four large systems: Squeak, JBoss, Azureus, and ArgoUML. We obtained several interesting results, among which, the identification of a set of recurring visual patterns that help maintainers: (a) more easily identify the role of and the way a package is used within the system (e.g., the package under analysis provides a set of layered services), and, (b) detect either problematic situations (e.g., a single package that groups together a large number of basic services) or opportunities for better package restructuring (e.g., removing cyclic dependencies among packages). The visualization generally scaled well and the detection of different patterns was always possible.Conclusion: The proposed visualizations and patterns proved to be useful in understanding and maintaining the different systems we addressed. To generalize to other contexts and systems, a real user study is required.

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“…The authors applied the visualization to analyze historical relationships among modules of a large telecommunications system and showed that it supported the understanding of the system architecture. Pinzger et al [47] proposed a visualization technique based on Kiviat diagrams. The visualization provides integrated views on source code metrics in different releases of a software system together with coupling information computed from CVS log files.…”

Section: Design-level Visualizationmentioning

confidence: 99%